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Cited by 69Embodied and Extended CognitionPhilosophy and Theoretical SciencePhilosophy and History of Science

From symbols to icons: the return of resemblance in the cognitive neuroscience revolution

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Williams, Daniel, Colling, Lincoln·2018·Synthese
DOI 10.1007/s11229-017-1578-6
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Abstract

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini (Synthese 193(5):1509–1534. doi: 10.1007/s11229-015-0783-4 , 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves (“from below”), and from a greater appreciation of the problems solved by bio-cognitive systems, chiefly regulation and prediction (“from above”). We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of (predictive) dynamical models as a central postulate in neurocognitive research.

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  • The FEP's universal scope and mathematical generality render it effectively unfalsifiable.

    “Williams (2018); Williams and Colling (2018); Williams (2020) argued that the FEP’s universal scope and mathematical generality render it effectively unfalsifiable.”

    From The myth of the Bayesian brain (2025) · cites this paper as Williams 2018

    Section 5.1 describes predictive processing as a global theory of brain function grounded in broad principles from statistical physics and theoretical biology. But the authors explicitly decline to assess its truth; they do not argue that its scope or mathematical generality makes the FEP unfalsifiable. No page number is available in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The FEP's universal scope and mathematical generality render it effectively unfalsifiable.

    “Williams (2018); Williams and Colling (2018); Williams (2020) argued that the FEP’s universal scope and mathematical generality render it effectively unfalsifiable.”

    From The myth of the Bayesian brain (2025) · cites this paper as Williams and Colling 2018

    Section 5.1 describes predictive processing as a global theory of brain function and links its approach to statistical physics and mathematical frameworks. But the paper does not argue that the free-energy principle’s breadth or mathematical generality makes it unfalsifiable; it explicitly declines to take a stand on the truth of predictive processing. No page numbers are available in our copy. (LLM, conf=0.80)

    Experimental — accuracy not yet measured

  • The Bayesian explanation of schizophrenia risks functioning more as a narrative scaffold than a mechanistic theory of pathophysiology.

    “The Bayesian explanation, while mathematically elegant, risks functioning more as a narrative scaffold than a mechanistic theory of pathophysiology (Williams 2018).”

    From The myth of the Bayesian brain (2025) · cites this paper as Williams 2018

    Section 5.1, “Homeostatic prediction machines” (page unavailable), says predictive processing is currently “at best a mechanism sketch,” with only tentative proposals for its realization in cortical circuitry. That supports the concern about mechanistic detail in a general theory of brain function, but the passage does not address schizophrenia, its pathophysiology, or the characterization “narrative scaffold.” (LLM, conf=0.87)

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Cites

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Research works printed but not linked: 7

  1. Clark, A. (1997). Being there. Cambridge, MA: MIT Press.
  2. Clark, A. (2016). Surfing uncertainty. Oxford: Oxford University Press.
  3. Craver, C. F. (2007). Explaining the brain. Oxford: Oxford University Press.
  4. Hempel, C. G., & Oppenheim, P. (1953). The logic of explanation. In H. Feigl & M. Brodbeck (Eds.), Readings in the philosophy of science (pp. 319-352). New York: Appleton-Century-Crofts, Inc.
  5. Llinás, R. R. (2001). I of the vortex. Cambridge, MA: MIT Press.
  6. Marcus, G. (2003). The algebraic mind. Cambridge, MA: MIT Press.
  7. Pearl, J. (2009). Causality (2nd ed.). Cambridge: Cambridge University Press.

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Related

Authors
Williams, D., & Colling, L.
Affiliations
University of Cambridge (GB)
Corresponding author
  • Williams, Daniel
  • Colling, Lincoln
ORCID
Venue
Synthese
Publisher
Springer Science and Business Media LLC
Published
2017-10-13
Volume
195
Issue
5
Pages
1941-1967
ISSN
  • 0039-7857 (ISSN-L)
  • 1573-0964
DOI
10.1007/s11229-017-1578-6
OpenAlex
W2763012326
Cited by
69
References
138
Citations by year
2026: 4 · 2025: 2 · 2024: 3 · 2023: 8 · 2022: 6 · 2021: 8 · 2020: 27 · 2019: 4 · 2018: 7
FWCI
5.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Synthese (publishedVersion, cc-by)
  • Sussex Research Online (University of Sussex) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo (cc-by)
Funders
Arts and Humanities Research Council
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
Keywords
mental representation, icons, similarity, representational similarity analysis, mechanistic explanation, information processing, regulation, prediction

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